AI made Simple,
Sustainable, and local
MinimAl empowers organizations to deploy powerful embedded Al without cloud dependency, complexity, or high energy.
MinimAl empowers organizations to deploy powerful embedded Al without cloud dependency, complexity, or high energy.
Models run on microcontrollers consuming < 1W, reducing energy use by 100x compared to cloud.
Compatible with popular hardware. Get started in minutes with auto-detection.
Deploy sophisticated Al without programming expertise through our intuitive UI.
Your data never leaves your devices. No cloud means no privacy concerns.
Community-driven marketplace for sharing optimized models and datasets.
Small businesses and labs can deploy Al without data scientists. Large enterprises reduce carbon footprint while performance. Innovators get a modular AloT solution that scales from prototype to production.
Request a demo 
Our intuitive interface guides you through connecting devices, selecting pre-trained modes, end infiguring actions—all without writing code.
Our intuitive interface guides you through connecting devices, selecting pre-trained modes, end infiguring actions—all without writing code.
/Supports ARM Cortex-M, RISC-V, and ESP32 architectures
/Runs on devices with as little as 256KB RAM
/Latency < 10ms for most inference tasks
/Supports ARM Cortex-M, RISC-V, and ESP32 architectures
/Runs on devices with as little as 256KB RAM
/Latency < 10ms for most inference tasks
Dr Sophie Laurent Research Director at INRIA « MinimAl represents a breakthrough in making Al both accessible and sustainable. Their approach to tinyML has enabled research projects that simply weren’t feasible with traditional cloud-based A/. »
Pierre Dubois Iot Lead at EDF « By implementing MinimAl for analog meter reading, we reduced our energy consumption by 98% compared to our previous cloud-based solution while maintaining 99.7% accuracy. »
Emma Chen CTO at GreeTech Solutions « The no-code interface allowed our field technicians to deploy Al models for predictive maintenance without needing data science expertise—a game changer for our.
Emma Chen CTO at GreeTech Solutions « The no-code interface allowed our field technicians to deploy Al models for predictive maintenance without needing data science expertise—a game changer for our.
Emma Chen CTO at GreeTech Solutions « The no-code interface allowed our field technicians to deploy Al models for predictive maintenance without needing data science expertise—a game changer for our.
EDF needed to digitize millions of analog meters without replacing existing infrastructure. MinimAl enabled them to retrofit smart reading capabilities using low- power edge devices, reducing energy consumption by 98% compared to their previous cloud-based solution.
Join our growing community of innovators deploying sustainable Al at the edge.
Join Our Early Access ProgramReady to deploy sustainable Al at the edge? Our team will get back to you within 24 hours.
